REDUCING ALERT FATIGUE IN SOC TEAMS THROUGH CONTEXTUAL PRIORITIZATION AND THREAT INTELLIGENCE INTEGRATION

Alert fatigue is becoming a bigger problem for Security Operations Center (SOC) personnel, which results in inefficiencies, missing important threats, and analyst burnout. It is challenging for analysts to concentrate on real threats since traditional alert triage systems produce an excessive amount of messages, many of which are low-priority or false positives. In order to overcome this difficulty, this conceptual paper suggests the Federated Adaptive Contextual prioritizing Framework (FACPF), which combines explainable AI, reinforcement learning, federated learning, and graph-based contextual prioritizing into a single, intelligent alert management system. To guarantee that severe threats are addressed right away, the suggested structure categorizes warnings according to severity, asset criticality, historical trends, and real-time threat intelligence feeds. SOC teams can get collaborative threat detection without disclosing sensitive information by utilizing Federated Learning (FL) for privacy-preserving intelligence sharing and Graph Neural Networks (GNNs) for contextual prioritizing. Additionally, Explainable AI (XAI) guarantees decision-making transparency, while Reinforcement Learning (RL) continuously improves alert prioritizing by integrating analyst feedback. The theoretical potential of FACPF to increase SOC efficiency through lowering false positives, speeding up reaction times, and maximizing analyst workload is described in this conceptual paper. Cybersecurity resilience is greatly increased by the framework’s smooth interface with SIEM/SOAR platforms, which permits automated threat response and real-time decision assistance. This research offers an organized road map for companies looking to reduce warning fatigue and optimize SOC operations using sophisticated AI-driven contextual prioritization, even though empirical validation is necessary.